Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

Roelfsema, Chris M., Lyons, Mitchell, Kovacs, Eva M., Maxwell, Paul, Saunders, Megan I., Samper-Villarreal, Jimena and Phinn, S.R. (2014) Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach. Remote Sensing of Environment, 150 172-187. doi:10.1016/j.rse.2014.05.001


 
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Author Roelfsema, Chris M.
Lyons, Mitchell
Kovacs, Eva M.
Maxwell, Paul
Saunders, Megan I.
Samper-Villarreal, Jimena
Phinn, S.R.
Title Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach
Journal name Remote Sensing of Environment   Check publisher's open access policy
ISSN 0034-4257
1879-0704
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.rse.2014.05.001
Volume 150
Start page 172
End page 187
Total pages 16
Place of publication New York, NY United States
Publisher Elsevier
Language eng
Abstract The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100m2) scales. However, landscape scale (>100km2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142km2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.
Keyword Biomass
Change
Cover
Dynamics
High spatial resolution
Object based
Seagrass
Species
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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